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Record W2967956311 · doi:10.24908/iqurcp.13297

Setting Boundaries: Lessons on When to Not Make Theatre

2019· article· en· W2967956311 on OpenAlexaffvenueabout
Allie Fenwick

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsSituatedTabooIndigenousNegotiationSociologyNarrativeAestheticsMedia studiesGender studiesPolitical scienceHistoryArtSocial scienceLawLiterature

Abstract

fetched live from OpenAlex

A common thread throughout much of Canada’s current theatrical output is that it asks audiences to think deeply about themselves and their connection to the material. Working with the idea that theatre has an important social and educational impact, my essay examines the need to set boundaries for theatre content. These boundaries are meant to function as a set of guidelines for managing controversial artistic choices, especially in a culture where artistic freedom and productive controversy are at stake. Some of the criteria I have developed for how to set boundaries include: how the work represents its subjects; what Canadians see as taboo; personal limitations individuals set for themselves; and, contentious timing. My research examines recent case studies, starting with Robert Lepage’s Kanata, which aimed to discuss Canada’s settler history, however, was cancelled in Canada after Indigenous artists and activists wrote an open letter concerning the lack of Indigenous presence in the cast and production team. I then move to an example that challenges my proposed model with Prom Queen: The Musical, a play about an Ontario student and his boyfriend battling the Catholic school board to go to prom together. I argue that although the play may not represent the values of the school board, adequate representation of the board’s ideals should be disregarded due to their anti-LGBTQ2+ beliefs. Through these examples and more, my research found that there are certain boundaries that should not be crossed in theatre. However, determining where these boundaries are situated remains in constant negotiation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.429
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0380.062
Scholarly communication0.0270.025
Open science0.0060.012
Research integrity0.0080.014
Insufficient payload (model declined to judge)0.0140.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.126
GPT teacher head0.358
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes3
Has abstractyes

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